Therapeutic planning for individualized management of breast cancer relies on patient stratification based on risk conferred by clinicopathologic factors. Prognostic and predictive markers commonly used for assessing risk associated with a breast tumor or its “aggressive potential”, include expression status of cell proliferation marker Ki67, estrogen receptor (ER), progesterone receptor (PR), extent of amplification of Human Epidermal Growth Factor Receptor 2 gene, and mitotic index (MI) of the tumor.
The mitotic index (MI) is determined by counting the number of mitotic cells per 10 HPFs (high-power fields) in a section of tumor tissue and has been shown to carry a strong prognostic value. Literature reports indicate that error-prone divisions of tumor cells lead to chromosomal instability to enable generation of genetic diversity out of which superlative karyotypes can be eventually selected. Thus, the higher the mitotic frequency within the proliferative population of tumor cells, the higher the probability of aggressive clones emerging to fuel tumor progression. The mitotic score within a tumor is therefore a crucial indicator of the risk of acquiring an aggressive phenotype. However, mitosis (M-phase) is only a part of the whole proliferative cycle and is relatively infrequent, as reflected in a mean tumor doubling time of 45-325 days. Infrequent mitoses underlie the failure of drugs that specifically target the M-phase in neoplastic cells.
Another prognostic factor, the Ki67 index (KI) is defined as the percentage of Ki67-positive neoplastic cells. Ki67 protein is present during all cell cycle phases (G1, S, G2 and M) characteristic of cell proliferation. As an adjunct to tumor-grading, pathologists have long been using Ki67 immunohistochemical staining to quantify the proliferating cell population within tumors. The percentage of Ki67-positive nuclei (referred to as Ki67 Index or KI) yields crucial information about disease prognosis, predicts relative responsiveness to chemotherapy, estimates residual risk in patients on standard therapy, and serves as a dynamic biomarker for neoadjuvant treatment efficacy.
Although KI is a universally accepted prognostic marker for cell proliferation, there is tremendous ambiguity in the nomenclature of proliferation cells in diagnostic pathology. In particular, the terms “actively proliferating”, “actively dividing” and “mitotically active” cells are often used synonymously. However, a cell in the “proliferation cycle” may not be actually “dividing”, whereas an “actively dividing” cell is indeed “proliferating.”
Among the above-described markers, MI is an integral component of the Nottingham Grading System (NGS), which is a modification of the Scarff-Bloom-Richardson breast tumor-grading system. KI measurement is not routinely mandated according to ASCO guidelines and KI has never been integrated into NGS. Extensive research has focused on evaluating KI and MI either separately or comparatively as markers of prognosis, yet surprisingly the two indices have never been studied integratively.
Tumor-grading in NGS involves microscopically evaluating three histological parameters, including tubule formation, nuclear pleomorphism, and mitotic activity/10 high-power fields (HPF), and assigning a score of 1 to 3 for each of them: tubule formation (>75%=1, 10% to 75%=2, and <10%=3), nuclear pleomorphism (none=1, moderate=2, and marked=3); and mitotic activity found in 10 HPF, based on a HPF size of 0.274 mm2 (<7 mitoses=1, 7 to 14 mitoses=2, and >14 mitoses=3). Summation of the three scores thus obtained (ranging from 3 to 9) determines the placement of the tumor into one of three Nottingham Grades. A combined score of 3, 4, or 5=Nottingham Grade (NG) I; a combined score of 6 or 7=NG II; and a combined score of 8 or 9=NG III. Multivariate analyses in large cohorts of breast cancer patients have consistently demonstrated that histologic grade of a tumor is a powerful prognostic indicator of disease recurrence and patient death independent of lymph node status and tumor size.
Despite widespread use of NGS by clinicians for patient stratification, prognostic heterogeneity persists within each Nottingham Grade. One drawback of the NGS is that about 30-60% of breast tumors are categorized as Nottingham grade (NG) II (the intermediate between the lowest grade of NG I and the highest grade of NG III), a classification that is not too informative for therapeutic decision-making. Gene expression studies suggest that many of these tumors are much more similar to NG I or NG III tumors in terms of their expression profiles, implying that many NG II patients may be either overtreated or undertreated. Also, the recommendation of cytotoxic chemotherapy for all invasive lesions is far from ideal when one considers that node-negative tumors smaller than 10 mm have survival rates of >90% without chemotherapy. Hence there is a need to refine the NGS and enhance its prognostic accuracy by identifying quantifiable biomarkers for breast tumors that (i) can discriminate more sharply the risk posed by breast tumors, (ii) can be accurately and reliably determined via a clinically-facile method, (iii) are robust and applicable in some, if not all, of the subtypes of breast carcinomas, and (iv) yield more accurate patient stratification.
The accuracy of NGS cannot be improved unless the precision in determining its constituent parameters is enhanced. One source of error pertains to mis-estimation of mitotic cells due to visual recognition from hematoxylin-eosin (H&E)-stained slides (an inherently error-prone process) and subjectivity (both intra- and inter-observer) arising from different choices of regions to be assessed.
A second source of error pertains to current diagnostic practices that take MI and KI into consideration as independent entities, while in reality, mitosis is a cell-cycle phase snugly nested within the proliferative cycle. In the absence of a unified view of mitosis and proliferation, the kinetic information on how fast the proliferative tumor cell population is actually cycling is lost. There is a need to improve the accuracy of tumor grading and more optimal selection of therapies.